1. Field of the Invention
The present invention provides a noninvasive, quantitative test for prognosis determination in cancer patients. The test relies on measurements of the tumor levels of certain messenger RNAs (mRNAs) or the corresponding gene expression products. These mRNA or protein levels are entered into a polynomial formula (algorithm) that yields a numerical score, which indicates recurrence risk (recurrence score) or the likelihood of patient response to therapy (response score).
2. Description of the Related Art
A need exists for clinical tests that help oncologists make well reasoned treatment decisions. One of the most fundamental decisions an oncologist faces in everyday practice is whether to treat or forego treatment of a particular patient with chemotherapeutic agents. Current therapeutic agents for cancer generally have modest efficacy accompanied by substantial toxicity. Thus, it is highly desirable to predetermine which patients (post-resection of the primary tumor) are likely to have metastatic recurrence. If there were a reliable way to obtain this information, high risk patients could be selected for adjuvant chemotherapy and patients unlikely to have cancer recurrence could be spared unnecessary exposure to the adverse events associated with chemotherapy. Similarly, before subjecting a patient (either before or after resection of a primary tumor) to a particular treatment, it would be desirable to know whether the patient is likely to respond to such treatment. For patients unlikely to respond to a certain treatment modality (e.g. treatment with certain chemotherapeutic agents and/or radiology), other treatments can be designed and used, without wasting valuable time.
Although modern molecular biology and biochemistry have revealed hundreds of genes whose activities influence the behavior and state of differentiation of tumor cells, and their sensitivity or resistance to certain therapeutic drugs, with a few exceptions, the status of these genes has not been exploited for the purpose of routinely making clinical decisions about drug treatments. One notable exception is the use of estrogen receptor (ER) protein expression in breast carcinomas to select patients for treatment with anti-estrogen drugs, such as tamoxifen. Another exceptional example is the use of ErbB2 (Her2) protein expression in breast carcinomas to select patients for treatment with the anti-Her2 antibody, Herceptin® (Genentech, Inc., South San Francisco, Calif.).
The present invention pertains to cancer, such as breast cancer, the biological understanding of which is still rudimentary. It is clear that the classification of breast cancer into a few subgroups, such as ErbB2+ subgroup, and subgroups characterized by low to absent gene expression of the estrogen receptor (ER) and a few additional factors (Perou et al., Nature 406:747-752 (2000) does not reflect the cellular and molecular heterogeneity of breast cancer, and does not permit optimization of patient treatment and care. The same is true for other types of cancers, many of which are much less studied and understood than breast cancer.
Currently, diagnostic tests used in clinical practice are single analyte, and therefore fail to capture the potential value of knowing relationships between dozens of different tumor markers. Moreover, diagnostic tests are frequently not quantitative, relying on immunohistochemistry. This method often yields different results in different laboratories, in part because the reagents are not standardized, and in part because the interpretations are subjective and cannot be easily quantified. RNA-based tests have not often been used because of the problem of RNA degradation over time and the fact that it is difficult to obtain fresh tissue samples from patients for analysis. Fixed paraffin-embedded tissue is more readily available and methods have been established to detect RNA in fixed tissue. However, these methods typically do not allow for the study of large numbers of genes from small amounts of material. Thus, traditionally, fixed tissue has been rarely used other than for immunohistochemistry detection of proteins.
In the past few years, several groups have published studies concerning the classification of various cancer types by microarray gene expression analysis {see, e.g. Golub et al., Science 286:531-537 (1999); Bhattachaijae et al., Proc. Natl. Acad. Sci. USA 98:13790-13795 (2001); Chen-Hsiang et al., Bioinformatics 17 (Suppl. 1):S316-S322 (2001); Ramaswamy et al., Proc. Natl. Acad. Sci. USA 98:15149-15154 (2001)). Certain classifications of human breast cancers based on gene expression patterns have also been reported {Martin et al., Cancer Res. 60:2232-2238 (2000); West et al., Proc. Natl. Acad. Sci. USA 98:11462-11467 (2001)}. Most of these studies focus on improving and refining the already established classification of various types of cancer, including breast cancer. A few studies identify gene expression patterns that may be prognostic {Sorlie et al., Proc. Natl. Acad. Sci. USA 98:10869-10874 (2001); Yan et al., Cancer Res. 61:8375-8380 (2001); Van De Vivjer et al. New England Journal of Medicine 347: 1999-2009 (2002)}, but due to inadequate numbers of screened patients, are not yet sufficiently validated to be widely used clinically.